The following description relates to reducing an amount of data in memory to allow for more efficient use and storage of data. In particular, the following description relates to reducing redundant data in repositories and databases.
Companies oftentimes store large amounts of data. The data can consume significant information technology resources of a business. For example, businesses may need to spend a portion of their resources on computers for workers, servers, databases, and systems to store the data. Businesses may have to allocate resources and personnel for the management of the data, and the ability to turn their data into useful organizational knowledge.
Certain amounts of business data are redundant. For example, companies often store large volumes of business data in relational database tables (e.g., fact tables). However, these fact tables often include interdefined or interrelated columns that occupy more memory space than required for the data they contain. The data redundancy has the consequence that application components, such as search engines working with the fact tables, can require more time and memory than necessary to obtain their results. If the amount of redundant data can be reduced and/or structured in such a way that the amount of available data can be used (or reused) more efficiently, then businesses can reduce the costs of storing and managing the data.